Wrinkle force microscopy: a machine learning based approach to predict cell mechanics from images

Li, Honghan; Matsunaga, Daiki; Matsui, Tsubasa S.; Aosaki, Hiroki; Kinoshita, Genki; Inoue, Kota; Doostmohammadi, Amin; Deguchi, Shinji

doi:10.1038/s42003-022-03288-x

Cited by 14 publications

(13 citation statements)

References 47 publications

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“…Since we conjecture that both topological defect- and disclination-mediated extrusion mechanisms are closely linked with stress localization, we characterize the in-plane and out-of-plane stresses associated with the simulated monolayer. We compute a coarse-grained stress field ( Christoffersen et al, 1981 ; Li et al, 2022 ),

, where

represents the center of the coarse-grained volume,

, corresponding to coarse-grained length

and unit vector

. Herein, the stress fields are computed using

.…”

Section: Resultsmentioning

confidence: 99%

Mechanical basis and topological routes to cell elimination

Monfared

Ravichandran

Andrade

et al. 2023

eLife

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Cell layers eliminate unwanted cells through the extrusion process, which underlines healthy versus flawed tissue behaviors. Although several biochemical pathways have been identified, the underlying mechanical basis including the forces involved in cellular extrusion remains largely unexplored. Utilizing a phase-field model of a three-dimensional cell layer, we study the interplay of cell extrusion with cell–cell and cell–substrate interactions in a flat monolayer. Independent tuning of cell–cell versus cell–substrate adhesion forces reveals that extrusion events can be distinctly linked to defects in nematic and hexatic orders associated with cellular arrangements. Specifically, we show that by increasing relative cell–cell adhesion forces the cell monolayer can switch between the collective tendency towards fivefold, hexatic, disclinations relative to half-integer, nematic, defects for extruding a cell. We unify our findings by accessing three-dimensional mechanical stress fields to show that an extrusion event acts as a mechanism to relieve localized stress concentration.

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, where

represents the center of the coarse-grained volume,

, corresponding to coarse-grained length

and unit vector

. Herein, the stress fields are computed using

.…”

Section: Resultsmentioning

confidence: 99%

Mechanical basis and topological routes to cell elimination

Monfared

Ravichandran

Andrade

et al. 2023

eLife

Self Cite

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“…Considering the complexity of the cellular biological mechanisms along with the physical/biological process associated with SFs dynamics and the limitation of the throughput in experiments, we believe this “installable” knowledge of conversion from cell geometry to SFs could be a powerful system, since this approach provides a virtual environment that can be applied to quantitative analysis on the correlations between the cell morphologies and the corresponding actin distribution characteristics. By combining with our previous study [52, 53], which is a machine learning based approach that can extract cellular force distributions from microscope images, we are planning to analyze how cell morphologies are connected with the cell mechanics (see supplementary S3). Our approach provides a powerful and cascadable framework to understand the geometric features of cells, which would support new findings in the field of cell biology and mechanobiology.…”

Section: Discussionmentioning

confidence: 99%

Knowledge “installed” diffusion model predicts the geometry of actin cytoskeleton from cell morphology

Liu

Deguchi

et al. 2023

Preprint

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Cells exhibit various morphological characteristics due to their physiological activities, and changes in cell morphology are inherently accompanied by the assembly and disassembly of the actin cytoskeleton. Stress fibers are a prominent component of the actin-based intracellular structure and are highly involved in numerous physiological processes, e.g., mechanotransduction and maintenance of cell morphology. Although it is widely accepted that variations in cell geometry interact with the distribution and localization of stress fibers, it remains unclear if there are underlying geometric principles between the cell morphology and actin cytoskeleton. Here we present a machine learning system, which uses the diffusion model, that can convert the cell shape to the distribution of stress fibers. By training with corresponding datasets of cell shape and stress fibers, our system learns the conversion to generate the stress fiber images from its corresponding cell shape. The predicted stress fiber distribution has good agreement with the experimental data, and the overlap region of predicted and experimentally observed stress fibers reaches 79.3 ± 12.4%. With this "installed" conversion relation between cellular morphology and corresponding stress fibers' localization, our system could perform virtual experiments that provide a visual map showing the probability of stress fiber distribution from the virtual cell shape. Our system provides a powerful approach to seek further hidden geometric principles between the cell morphologies and actin cytoskeletons.

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“…According to Harris and colleagues, when cells are cultured on a soft elastic substrate like a thin sheet of silicon, they exert a traction force responsible for elastic distortion and wrinkling of the substrate [82]. Individual wrinkles change during cell migration owing to changes in cellular forces and can be detected using a machine learning approach [83].…”

Section: Biophysical Assessment Of Cell Mechanicsmentioning

confidence: 99%

“…Recently, many fundamental and technological advancements in TFM have been made to control biochemical responses and mechanotransduction at the cell-matrix interface as well as the mechanobiology behind the results. Therefore, a new artificial intelligence algorithm using machine learning to calculate the cellular force distribution from microscopy images has also been utilized [83]. In the first phase of their study, cells were cultured on an elastic substrate, and the cellular traction corresponding to the substrate wrinkle was measured, followed by the measurement of the traction force using TFM.…”

Section: Working Principle Of Traction Force Microscopymentioning

confidence: 99%

The role of cellular traction forces in deciphering nuclear mechanics

et al. 2022

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Cellular forces exerted on the extracellular matrix (ECM) during adhesion and migration under physiological and pathological conditions regulate not only the overall cell morphology but also nuclear deformation. Nuclear deformation can alter gene expression, integrity of the nuclear envelope, nucleus-cytoskeletal connection, chromatin architecture, and, in some cases, DNA damage responses. Although nuclear deformation is caused by the transfer of forces from the ECM to the nucleus, the role of intracellular organelles in force transfer remains unclear and a challenging area of study. To elucidate nuclear mechanics, various factors such as appropriate biomaterial properties, processing route, cellular force measurement technique, and micromanipulation of nuclear forces must be understood. In the initial phase of this review, we focused on various engineered biomaterials (natural and synthetic extracellular matrices) and their manufacturing routes along with the properties required to mimic the tumor microenvironment. Furthermore, we discussed the principle of tools used to measure the cellular traction force generated during cell adhesion and migration, followed by recently developed techniques to gauge nuclear mechanics. In the last phase of this review, we outlined the principle of traction force microscopy (TFM), challenges in the remodeling of traction forces, microbead displacement tracking algorithm, data transformation from bead movement, and extension of 2-dimensional TFM to multiscale TFM.

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Wrinkle force microscopy: a machine learning based approach to predict cell mechanics from images

Cited by 14 publications

References 47 publications

Mechanical basis and topological routes to cell elimination

Mechanical basis and topological routes to cell elimination

Knowledge “installed” diffusion model predicts the geometry of actin cytoskeleton from cell morphology

The role of cellular traction forces in deciphering nuclear mechanics

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